一种自适应无线传感器网络监控系统网关设计

针对无线传感器网络网关通信方式单一、灵活性差的问题,设计并实现了一种自适应WSN监控系统网关,网关能够根据工作环境选择以太网、WiFi、GPRS通信方式中的一种来转发无线传感器网络的数据;基于STM32F107微处理器完成了网关的硬件开发和软件设计,实现了数据汇集、通信方式的选择、远程通信等功能;基于VC++完成了监控中心软件的开发,实现了数据接收、图形化显示、存储、查询等功能;搭建校园温湿度监控系统对网关性能进行测试,测试结果表明,该网关有扩展灵活、可靠性高、使用方便等特点。     

小型连锁超市购物卡系统的开发

本系统适用于小型连锁超市进行商品销售或购物卡的管理。其主要功能包括购物卡的制作管理、发行管理、信息查询、系统维护。本系统界面友好、直观,操作简便,系统中的多级权限管理等也可以保证数据的安全可靠。     

500-fps face tracking system

In this paper, we propose a high-speed vision system that can be applied to real-time face tracking at 500 fps using GPU acceleration of a boosting-based face tracking algorithm. By assuming a small image displacement between frames, which is a property of high-frame rate vision, we develop an improved boosting-based face tracking algorithm for fast face tracking by enhancing the Viola–Jones face detector. In the improved algorithm, face detection can be efficiently accelerated by reducing the number of window searches for Haar-like features, and the tracked face pattern can be localized pixel-wise even when the window is sparsely scanned for a larger face pattern by introducing skin color extraction in the boosting-based face detector. The improved boosting-based face tracking algorithm is implemented on a GPU-based high-speed vision platform, and face tracking can be executed in real time at 500 fps for an 8-bit color image of 512 × 512 pixels. In order to verify the effectiveness of the developed face tracking system, we install it on a two-axis mechanical active vision system and perform several experiments for tracking face patterns.     

A new video watermarking algorithm based on shot segmentation and block classification

In this paper, we propose a new video watermarking algorithm based on shot segmentation and block classification to enhance the robustness, imperceptibility and real-time performance based on the H.264/AVC codec. A method of selecting host frames is proposed based on shot segmentation to avoid embedding watermark frame by frame, so as to improve the robustness and the real-time performance. The watermark signal is cropped into small watermarks according to the number of shots in the host video, and small watermarks are respectively embedded into different shots. The watermarking capacity and the perceptual quality are greatly improved by this way. A method of selecting host coefficients is proposed based on block classification in the Discrete Cosine Transformation (DCT) compressed domain. The texture characteristics of host blocks are considered in the classification and the places of host coefficients can change adaptively according to the content of the video. The imperceptibility of the watermarked video is greatly improved by this way. The simplified quantization index modulation (QIM) is applied to embed watermark. It brings fewer artifacts to the host signal than the current main watermarking method, such as spread spectrum (SS), differential energy watermarking (DEW) and so on. The experiment results show that the proposed scheme has a good performance in maintaining real-time performance and resisting Gaussian noising, frame swapping, MPEG compression, etc.     

Financial time series forecasting using LPP and SVM optimized by PSO

In this paper, a predicting model is constructed to forecast stock market behavior with the aid of locality preserving projection, particle swarm optimization, and a support vector machine. First, four stock market technique variables are selected as the input feature, and a slide window is used to obtain the input raw data of the model. Second, the locality preserving projection method is utilized to reduce the dimension of the raw data and to extract the intrinsic feature to improve the performance of the predicting model. Finally, a support vector machine optimized using particle swarm optimization is applied to forecast the next day’s price movement. The proposed model is used with the Shanghai stock market index and the Dow Jones index, and experimental results show that the proposed model performs better than other models in the areas of prediction accuracy rate and profit.     

不同横断面泄流槽的地震堰塞湖溃决实验研究

堰塞湖的溃决问题是研究堰塞湖的核心内容之一,也是堰塞湖进行排险和处置的重要依据.本文以唐家山堰塞湖为原型,设计室内1∶250的近似模型实验,完成了堰塞湖自然溃决实验、梯形槽堰塞湖溃决实验、三角形槽堰塞湖溃决实验和复式槽堰塞湖溃决实验,通过对溃决过程中堰塞坝内部土体体积含水量变化特征、泄流槽侵蚀特征和溃决流量过程的分析,获得以下认识:(1)溃决初期以下切侵蚀为主、后期以侧蚀为主;溯源侵蚀方面,自然溃决表现最强烈,人工辅助溃决实验中溯源侵蚀由强到弱依次为:复式槽、三角形槽、梯形槽;(2)人工辅助溃决在一定程度上削减了洪峰流量,与自然溃决相比减小了7.7％ ～20.1％,表明开挖人工泄流槽降低坝前水位的措施对于降低堰塞湖溃决风险是可行的;(3)以溃决流量增长率高或到达溃决洪峰时间短为判定溃决初期排泄效率高的标准,对比3组人工槽溃决实验,排泄效率由高到低依次为:三角型横断面、复式横断面、梯形横断面.针对唐家山堰塞湖处置,可在梯形槽基础上开挖三角形槽变为复式槽来进一步提升溃决初期排泄效率.     

针对短信猫接收短信的安全风险分析

目前,利用短信攻击和威胁手机安全的案例层出不穷,随着技术的进步和短信猫应用的发展,垃圾短信和病毒短信等将同样威胁到短信猫及与其相连的应用系统的安全。从GSM短信工作原理出发,具体分析了不同的短信类别及相关的安全风险,最后针对短信猫接收短信的安全风险提出了相应的解决方案。     

基于粗糙度的近似概念格动态分类集成学习模型研究与应用

概念格(Galois格)是一种进行数据分类学习的有效工具,然而建格规模庞大使分类效率和准确率受到较大影响.将粗糙度理论应用到概念格分类问题研究中,提出一种新型的近似概念格动态建格和分类挖掘集成学习模型(CACLR).该模型在粗糙度区间根据样本空间分布构建多个相对独立分布且比较精确的近似概念格分类器,能及时消除建格过程中大量与分类知识无关的节点,有效缩减原格规模,融合得到的分类挖掘集成学习模型,具有较好的粗糙分类精度和知识预测学习能力.最后进行CACLR分类集成学习模型在标准UCI数据集中的对比实验,有效验证了该模型的实用价值.     

一种用于线性稳压器的限流电路

介绍一种电路结构简单、限流保护功能可靠、可用于线性稳压电路的限流保护电路,在0.6μm CMOS工艺下流片,测量结果显示该限流电路可在驱动输出超过额定值(100mA)后,有效启动以保护电源芯片安全.     

利用语句结构信息的机器翻译自动评测初探

机器翻译评测对机器翻译有着极其重要的意义,它对翻译系统性能的提高做出了很大的贡献,同时促进了机器翻译的发展.本文在HNC机器翻译策略的基础上,对翻译语料的句类转换和句式转换做了初步的研究,并利用句类转换和句式转换的理论,建立了基于句类信息的自动评测的打分机制.